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检索条件"主题词=Unit commitment problem"

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A hybrid multi-objective evolutionary algorithm for economic-environmental generation scheduling

A hybrid multi-objective evolutionary algorithm for economic...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Tsalavoutis, Vasilios Vrionis, Constantinos Tolis, Athanasios Natl Tech Univ Athens Sch Mech Engn Athens Greece

ISBN: (纸本)9781450361118

In this study, a hybrid Multi-Objective Evolutionary Algorithm (MOEA) is proposed for the Multi-Objective Short-Term unit commitment (MO-STUC) problem, considering the minimization of operation cost and emissions as objectives. The proposed algorithm is based on a real-coded Differential Evolution (DE) and a two-step function to simultaneously deal with both the scheduling of the state of the units and the dispatching of the power among the committed units. Moreover, a local search technique, which combines two distinct local search procedures based on Pareto dominance and scalar fitness function, is hybridized with the proposed MOEA. In addition, a heuristic repair mechanism and a problem-specific mutation operator are adopted to enhance the algorithm's performance. The method is tested on two frequently studied test systems comprising 10 and 20 units, respectively. The non-dominated fronts provided by the proposed method adequately approximate the Pareto fronts of the test instances. Simulation results reveal that the proposed algorithm outperforms two standard MOEAs, based on DE and Genetic Algorithm, with respect to the employed quality indicators. Furthermore, the beneficial impact of combining the two local search procedures is demonstrated.

关键词： unit commitment problem Differential Evolution Multi-objective optimization Hybrid Metaheuristic Local Search

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Differential Evolution with Two Subpopulations 1

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5th International Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO)

作者： Lynn, Nandar Mallipeddi, Rammohan Suganthan, Ponnuthurai Nagaratnam Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Kyungpook Natl Univ Sch Elect Engn Daegu South Korea

ISBN: (数字)9783319202945

ISBN: (纸本)9783319202945;9783319202938

In this paper, differential evolution with two subpopulations is proposed for balancing exploration and exploitation capabilities. The first population is responsible for exploring over the search space to find good regions using only its own subpopulation. The second subpopulation is responsible for exploiting good regions. The exploitation-oriented sub-population is permitted to make use of the whole population to select best solution candidates to generate offspring. Hence, this heterogeneous one-way information transfer allows the exploration subpopulation to maintain diversity even when exploitation group converges. This is an efficient realization of population based algorithm enabling simultaneous use of highly exploitative and explorative characteristics simultaneously. Hence, this approach can be an effective substitute for memetic algorithms in the real-parameter optimization domain. The performance of the algorithm is evaluated using the shifted and rotated benchmark problems. To verify the performance of the proposed algorithm, it is also applied to solve the unit commitment problem by considering 10 and 20 unit power systems over 24 h scheduling period.

关键词： Differential evolution Subpopulations Exploration Exploitation Memetic algorithms Power systems unit commitment problem Scheduling

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An Improved Flexibility Metric Based on Kernel Density Estimators Applied on the Greek Power System 1

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21st International Symposium on High Voltage Engineering (ISH)

作者： Krommydas, Konstantinos F. Stratigakos, Akylas C. Dikaiakos, Christos Papaioannou, George P. Zafiropoulos, Elias Ekonomou, Lambros Univ Patras Dept Elect & Comp Engn Patras 26504 Greece Independent Power Transmiss Operator IPTO SA Res Technol & Dev Dept 89 Dyrrachiou & Kifisou Str Gr Athens 10443 Greece Inst Commun & Comp Syst 9 Iroon Polytech St Athens 15780 Greece

ISBN: (数字)9783030378189

ISBN: (纸本)9783030378189;9783030378172

The large-scale integration of variable renewable energy (VRE) in power systems, such as wind and solar, increases the flexibility needed to maintain the load-generation balance. In order for the power operators to plan for secure and reliable operation, they must examine whether there exists sufficient power system flexibility to meet ramps caused by the increased VRE integration and the system demand. In this context, the paper aims to propose an improved flexibility metric to accurately evaluate the flexibility level of a power system in the planning stage. The proposed metric is based on kernel density estimators and expresses the probability of the flexibility residual (the difference between the available flexibility and the expected net load ramps) being less than zero. The Greek power system is used as case study in order to evaluate the proposed index. In particular, the unit commitment optimization problem with flexibility constraints for ten different scenarios based on the ENTSO-E methodology for the time period 2020-2024 is solved and then the proposed metric is calculated. Finally, this index is compared to the well known insufficient ramping resource expectation (IRRE) metric to further evaluate it.

关键词： Flexibility metrics unit commitment problem Kernel function

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MULTI-OBJECTIVE PLACEMENT IN UCP BY IMPROVED HONEY BEE MATING OPTIMIZATION 6

MULTI-OBJECTIVE PLACEMENT IN UCP BY IMPROVED HONEY BEE MATIN...

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International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC)

作者： Sivaranjani, S. Nayanatara, C. Sri Sai Ram Engn Coll Dept Elect & Elect Engn Madras 44 Tamil Nadu India

ISBN: (纸本)9781509043248

Improved version of Honey Bee Mating Optimization (IHBMO) algorithm is used here to solve the unit commitment problem (UCP). Actually, the computation of the optimization process is too complex in the non linear scheduling problem. When large number of thermal units are employed, this problem becomes difficult to undertake in the power system. IHBMO technique is the combination of the genetic algorithm and simulated annealing which is used for the fast local search to obtain the best solution. To show the effect and robustness of the IHBMO a ten thermal unit system at various conditions is considered. The simulation result is obtained from the reference UC and algorithm results.

关键词： IHBMO unit commitment problem Optimization

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Day-ahead Optimal Operational and Reserve Power Dispatching in a PV-Based Urban Microgrid 18

Day-ahead Optimal Operational and Reserve Power Dispatching ...

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18th European Conference on Power Electronics and Applications (EPE)

作者： Yan, Xingyu Abbes, Dhaker Francois, Bruno Bevrani, Hassan Univ Lille Lab Electrotech & Elect Puissance Cent Lille HEIArts & Metiers ParisTechEA 2697 F-59000 Lille France Univ Kurdistan Fac Engn POB 416 Kurdistan Sanandaj Iran

ISBN: (纸本)9789075815245

Accurate sizing of power reserve (PR) due to inaccurate forecast of both renewable energy sources (RES) and load demand in a microgrid can provide substantial cost reductions. This paper proposes two strategies to dispatch the PR into different power generators. The first one uses only micro gas turbine (MGT). The second one uses MGTs plus PV based active generators (AGs). The latter enables the RES to cover the system load demand and PR for some period during a day. To implement those methods, firstly an urban microgrid with PV power generators is introduced. Then, day-ahead optimal planning with dynamic programming (DP) for unit commitment problem (UCP) is applied under several non-linear constraints. Finally, a case study application has been completed to verify the proposed methods.

关键词： Distributed generators Dynamic programming Energy management system Power reserve dispatching unit commitment problem

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unit commitment Using Hybrid Genetic Algorithm With Differential Evolution

Unit Commitment Using Hybrid Genetic Algorithm With Differen...

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Conference on Innovations in Power and Advanced Computing Technologies (i-PACT)

作者： Thakshaayene, J. R. Kavitha, D. Thiagarajar Coll Engn Year Dept Elect & Elect Engn 2 ME Power Syst Engn Madurai 625015 Tamil Nadu India Thiagarajar Coll Engn Dept Elect & Elect Engn Madurai 625015 Tamil Nadu India

ISBN: (纸本)9781509056828

The vital role in power system is that to optimally utilize the resources to meet the demand where the generation scheduling can be performed by unit commitment. In this paper the formulation of unit commitment has been discussed and the solution is obtained by the Conventional Dynamic Programming method. The Hybrid Genetic With Differential Evolution Algorithm was proposed. In this Hybrid algorithm a new strategy had been followed that the Genetic Algorithm had been performed for the Economic Dispatch problem and the results of the Economic Dispatch (manipulated by GA) has been incorporated in the unit commitment problem and then the problem was evaluated by Differential Evolution Algorithm. Comparatively the Hybrid Genetic With Differential Evolution Algorithm yields better results satisfying several unit commitment Constraints where the objective of minimum total cost had been obtained. The proposed algorithm is performed on 4 generating system and IEEE standard 10 generating unit system.

关键词： unit commitment unit commitment problem Dynamic Programming

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Stochastic unit commitment Model for Power System with Renewable Energy

Stochastic Unit Commitment Model for Power System with Renew...

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International Electrical Engineering Congress (iEECON)

作者： Kaewpasuk, Sukita Intiyot, Boonyarit Jeenanunta, Chawalit Chulalongkorn Univ Fac Sci Dept Math & Comp Sci Bangkok Thailand SIIT Sch Management Technol Pathum Thani Thailand

ISBN: (纸本)9781509046669

The renewable energy plays an important role in the modern power system. However, the power output from the renewable energy highly depends on the environments and therefore has uncertainty issue. In this paper, we propose a stochastic expected recourse model for the system with the renewable energy. The proposed model is applied to a case study and the effect of renewable energy on the power system is analyzed.

关键词： stochastic model unit commitment problem renewable energy uncertainty

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unit commitment by Genetic Evolving Ant Colony Optimization

Unit Commitment by Genetic Evolving Ant Colony Optimization

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World Congress on Nature and Biologically Inspired Computing

作者： Vaisakh, K. Srinivas, L. R. Andhra Univ AU Coll Engn Dept Elect Engn Visakhapatnam 530003 Andhra Pradesh India SRKR Engn Coll Dept Elect & Elect Engn Bhimavaram 534204 Andhra Pradesh India

ISBN: (纸本)9781424450534

Ant Colony Optimization (ACO) is more suitable for combinatorial optimization problems. This paper proposes Genetic Evolving Ant Colony Optimization (EACO) method for solving unit commitment (UC) problem. The EACO employs Genetic Algorithm (GA) for finding optimal set of ACO parameters, while ACO solves the UC problem. problem formulation takes into consideration the minimum up and down time constraints, start up cost, shut-down cost, spinning reserve, ramp rate constraints and generation limit constraints. The feasibility of the proposed approach is demonstrated for 4 and 10-unit systems. The test results are encouraging and are compared with those obtained by other methods.

关键词： Evolving ant colony optimization unit commitment problem pheromone matrix genetic algorithm

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unit commitment by selective self-adaptive ACO with relativity pheromone updating approach

Unit commitment by selective self-adaptive ACO with relativi...

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IPEC 2007 Meeting

作者： Chusanapiputt, Songsak Nualhong, Dulyatat Jantarang, Sujate Phoomvuthisarn, Sukumvit Department of Electrical Power Engineering Faculty of Engineering Mahanakorn University of Technology Thailand Electrical Engineer Department Electrical and Control System Engineering Division Electricity Generating Authority of Thailand (EGAT) Department of Computer Engineering Faculty of Engineering Mahanakorn University of Technology Thailand Center of Excellence in Electrical Power Technology Faculty of Engineering Chulalongkorn University Thailand IEEE

ISBN: (纸本)9789810594237

This paper presents a novel approach to solve the constrained unit commitment problem using the Selective Self-Adaptive Ant Colony Optimization (SSACO) for improving search performance by automatically adapting ant populations and their transition probability parameters, which cooperates with the Candidate Path Management Module (CPMM) and the Effective Repairing Heuristic Module (ERHM) in reducing search space and recovering a feasible optimality region so that a high quality solution can be acquired in a very early iterative. A new concept of the Relativity Pheromone Updating (RPU) is also introduced to provide a reasonable evaluation of the pheromone trail intensity among the agents. The proposed SSACO method not only enhances the convergence of search process, but also provides a suitable number of the population sharing which conducts a good guidance for trading-off between the importance of the visibility and the pheromone trail intensity. The proposed method has been performed on a test system up to 100 generating units with a scheduling time horizon of 24 hours. The numerical results show the most economical saving in the total operating cost when compared to the previous literature results. Moreover, the proposed SSACO topology can remarkably speed up the computational time of ant colony optimization, which is favorable for a large-scale UC problem implementation.

关键词： Ant Colony Optimization unit commitment problem optimization method

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Smart Grid Stochastic Optimization with Ant Colony-based Scenario Generation 3

Smart Grid Stochastic Optimization with Ant Colony-based Sce...

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3rd IFAC Workshop on Integrated Assessment Modeling for Environmental Systems (IAMES)

作者： Valderrama, Daniel Fernandez Ferro, Giulio Alonso, Juan Ignacio Guerrero De Mora, Carlos Leon Parodi, Luca Robba, Michela Univ Genoa Dept Informat Bioengn Robot & Syst Engn DIBRIS Genoa Italy Univ Seville Dept Elect Technol Escuela Politecn Super Seville Spain

This paper presents a stochastic optimization approach for the operational management of sustainable energy districts and polygeneration microgrids. Stochastic operation optimization allows for an uncertain approach to deal with imprecise variables. A new approach is here presented for generating scenarios based on Ant Colony Optimization (ACO) to assess the uncertainties related to inexact data, renewable energy sources and power demand. In fact, due to the uncertainties related to forecasting loads and renewables, it is necessary to analyze the probability of occurrence of the prediction and the different scenarios that could be faced. Then, based on the generated scenarios and probabilities, a scenario-based two-stage stochastic optimization approach has been formulated to optimize the operation strategies of the various technologies and solve the unit commitment problem under uncertainty. The developed models have been applied to the Savona Campus Smart Polygeneration Microgrid, and historical data from 2018 have been used. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： ACO forecasting scenario generation stochastic optimization unit commitment problem

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